Journal of Soft Computing Paradigm
IRO Journals
Volume 2 — Issue 1 — March 2020

Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network

https://doi.org/10.36548/jscp.2020.1.004
Samuel Manoharan
Professor, Department of Electronics, Bharathiyar College of Engineering and Technology, Thiruvettakudy, Karaikal
A. Sathesh
Department of EEE, Eritrea Institute of Technology
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Manoharan, Samuel, and A. Sathesh. 2020. “Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network”. Journal of Soft Computing Paradigm 2 (1): 36-46. https://doi.org/10.36548/jscp.2020.1.004.
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Keywords

— Data Mining
— Classification
— Feed Forward Networks
— PNN
— WCECP
Published: 30-03-2020

Abstract

The most vital step in mining data's in order to have a proper decision making is the classification, it is remains important in multiple of human activities such as the industrial applications, marketing campaigns, research process and the scientific endeavors. The process of classifying involves the objects categorization into classes that are already defined. These categorizations are developed according to the identical attributes of the items or the objects. Multitudes of methods were devised to improve the accuracy in the classification to devour an enhanced performance in terms of faster convergence speed. The algorithm based on water cycle that includes the evaporation, condensation and precipitation (WC-ECP), which is a population based metaheuristic is used in the paper to improve the accuracy in the feed forward neural network (PNN-probabilistic neural network) to standardizes its random constraint choice and in turn improvise the accuracy of the categorization and the speed of the convergence. The proposed method was tested with the five dataset of UCI machine learning repository and was evinced that the WCECP-PNN performed better compared to the other evolutionary algorithms such as the GA which is also a population based Meta-heuristics

References

  1. Specht, Donald F. "Probabilistic neural networks." Neural networks 3, no. 1 (1990): 109-118.
  2. Mahajan, Vishal, Richa Misra, and Renuka Mahajan. "Review of data mining techniques for churn prediction in telecom." Journal of Information and Organizational Sciences 39, no. 2 (2015): 183-197.
  3. Andreopoulou, Zacharoula, Christiana Koliouska, and Constantin Zopounidis. "Methodologies." In Multicriteria and Clustering, pp. 13-32. Springer, Cham, 2017.
  4. Johnston, Kyle B., and Hakeem M. Oluseyi. "Generation of a supervised classification algorithm for time-series variable stars with an application to the LINEAR dataset." New Astronomy 52 (2017): 35-47.
  5. Hernández-Lobato, José Miguel, and Ryan Adams. "Probabilistic backpropagation for scalable learning of bayesian neural networks." In International Conference on Machine Learning, pp. 1861-1869. 2015.
  6. Schaffer, J. David, Darrell Whitley, and Larry J. Eshelman. "Combinations of genetic algorithms and neural networks: A survey of the state of the art." In[Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks, pp. 1-37. IEEE, 1992.
  7. Smys, S. "Ddos Attack Detection In Telecommunication Network Using Machine Learning." Journal of Ubiquitous Computing and Communication Technologies (UCCT) 1, no. 01 (2019): 33-44.
  8. Sebt, M. H., M. R. Afshar, and Y. J. E. O. Alipouri. "Hybridization of genetic algorithm and fully informed particle swarm for solving the multi-mode resource-constrained project scheduling problem." Engineering Optimization 49, no. 3 (2017): 513-530.
  9. Kumar, Somesh, and Manu Pratap Singh. "Pattern recall analysis of the Hopfield neural network with a genetic algorithm." Computers & Mathematics with Applications 60, no. 4 (2010): 1049-1057.
  10. Raj, Jennifer S. "A Comprehensive Survey On The Computational Intelligence Techniques And Its Applications." Journal of ISMAC 1, no. 03 (2019): 147-159.
  11. Cruz, Dávila Patrícia Ferreira, Renato Dourado Maia, Leandro Augusto da Silva, and Leandro Nunes de Castro. "A bee-inspired data clustering approach to design RBF neural network classifiers." In Distributed Computing and Artificial Intelligence, 11th International Conference, pp. 545-552. Springer, Cham, 2014.
  12. Adio, Saheed Adewale, Mehdi Mehrabi, Mohsen Sharifpur, and Josua Petrus Meyer. "Experimental investigation and model development for effective viscosity of MgO–ethylene glycol nanofluids by using dimensional analysis, FCM-ANFIS and GA-PNN techniques." International Communications in Heat and Mass Transfer 72 (2016): 71-83.
  13. Manimala, K., and K. Selvi. "Power disturbances classification using S-transform based GA–PNN." Journal of The Institution of Engineers (India): Series B 96, no. 3 (2015): 283-295.
  14. Joseph, S. Iwin Thanakumar, and Iwin Thanakumar. "Survey of data mining algorithm’s for intelligent computing system." Journal of trends in Computer Science and Smart technology (TCSST) 1, no. 01 (2019): 14-24.